1. Field of the Invention
The present invention relates to a movement vector detection apparatus and, more particularly, to an apparatus for detecting movement vectors from an image signal.
2. Related Background Art
In the field of image pickup apparatuses for correcting vibrations of a video camera, detecting panning of a video camera, or detecting and tracing a movement of a specific image in a video camera, movements and kinds of movements of images must be accurately detected from image signals output from an image pickup means such as an image pickup device.
For example, an image vibration detection apparatus cannot distinguish a local movement of an object from a vibration of the entire field of view. In order to make it possible to distinguish a local movement from a vibration of the entire field of view, different detection sensitivities for image vibration amounts must be distributed within a frame area.
A conventional image vibration detection apparatus proposed in consideration of the above drawback is exemplified as an image vibration correction apparatus described in the Society of Television Techniques, Technical Report Vo. 11, No. 3, pp. 43-48, PPOE, '87-12 (May, 1987). In this apparatus, the entire frame is divided into 140 blocks, vibration detection blocks are arbitrarily turned on/off, and a representative point matching is performed for only vibration detection ON blocks.
The present applicant filed a movement detection apparatus as U.S. and EPC applications on Mar. 6, 1989 (U.S. patent application Ser. No. 319,658).
Movement vector detection methods using image signal processing are exemplified as a time and spatial gradient method described in Japanese Patent Publication No. 60-46878 and J. O. Limb and J. A. Murphy, "Measuring the Speed of Moving Objects from Television Signals", IEEE Trans. Com., Com-23, 4, p.p. 474-478 (April, 1975), or a matching method described in "MUSE Movement Vector Detection Apparatus", the Society of Television Technics, Technical Report p.p. 25-30 (issue date: May 24, 1985, Friday).
According to the time and spatial gradient method, a movement amount of each point is calculated by the following equations: EQU .alpha.=.SIGMA..sub.B d.multidot.sign(g'.sub.x)/.SIGMA..sub.B .vertline.g'.sub.x .vertline. EQU .beta.=.SIGMA..sub.B d.multidot.sign(g'.sub.y)/.SIGMA..sub.B .vertline.g'.sub.y .vertline.
where .alpha. and .beta. are the movement amounts in the x and y directions, respectively, d is the concentration difference between two continuous images as a function of time at the same position, i.e., a time gradient, and g'.sub.x and g'.sub.y are the spatial gradients in the x and y directions when the image is given as g. Note that .SIGMA..sub.B represents a total sum operation within a block, and sign() is a function of outputting signs of the gradients g'.sub.x and g'.sub.y.
A movement vector is calculated by the following equation according to the representative point matching: EQU P(i,j)=.SIGMA..sub.B .SIGMA..sub.B .vertline.g.sub.0 (x-i,y-i)-g.sub.1 (x,y).vertline.
where g.sub.0 (x,y) and g.sub.1 (x,y) are two continuous images as a function of time, and i and j are the movement amounts of the images, respectively.
More specifically, a cumulative value of the absolute values of the differences between the two images upon shifting of the image g.sub.0 (x,y) by the amounts i and j is obtained within a block as a unit operation area. A movement amount (i,j) which minimizes the vector P(i,j) is defined as a movement vector of the corresponding block. The calculation of P(i,j) may be performed by using a square of a difference or a nonlinear function in place of use of the absolute value of the difference.
When the time and spatial gradient method or the representative point matching method is used, a low-pass filter, having characteristics which prevent detailed information in an image from being lost, is used as a preprocessing filter. The use of the low-pass filter aims at smoothing a sharp edge portion of the image or reducing input image noise. The size and shape of blocks obtained by dividing an input image are normally predetermined and are decided independently of the characteristics of the preprocessing filter.
In the conventional example described above, a detection range is narrowed by a high-frequency component of an input image, and it is difficult to apply this conventional technique to an image having a large movement amount. In order to solve this problem, a low-pass filter having a sufficiently large mask is used, and the image is divided into large blocks including spatial gradients in various directions, thereby widening the detection range. According to this technique, however, the use of large blocks undesirably causes a decrease in resolution.
According to the above method, the detection range is narrowed by the high-frequency component of the input image, and it is difficult to apply this method to an image having a large movement amount.
A relationship between the total sum .SIGMA..sub.B within the block and the detection block will be taken into consideration. The size and shape of each block for the total sum .SIGMA..sub.B are generally predetermined. When the size of the block is increased, the detection range is increased accordingly. When the block is large, a possibility for including edges in various directions within one block is increased, and clear signals can be obtained against noise, and vector synthesis can have higher precision. These advantages are described in the above literatures and Shingaku Giho IE78-67 "Measurement of movement amount or speed of a moving object by image signals".
When the block size is increased in the conventional example, the number of blocks constituting one frame is reduced, so that the number of vectors to be detected is reduced. Therefore, slight pattern movements cannot be easily detected.